Prof. Arman Rahmim Profile

Prof. Arman Rahmim

Professor at BC Cancer Research Institute

SPIE Involvement:

Author

Publications (25)

Proceedings Article | 7 April 2023 Poster + Paper

Clinician-interactive AI for RECIST measurements in CT imaging

Luke Polson, Ivan Klyuzhin, Ren Yuan, Monty Martin, Isaac Shiri, Habib Zaidi, Carlos Uribe, Arman Rahmim

Proceedings Volume 12463, 124633N (2023) https://doi.org/10.1117/12.2654320

KEYWORDS: Computed tomography, Artificial intelligence, Education and training, Cancer, Data modeling, Image segmentation, Lung cancer, Tumor growth modeling, Oncology, Deep learning

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Proceedings Article | 3 April 2023 Presentation + Paper

Observer study-based evaluation of TGAN architecture used to generate oncological PET images

Roberto Fedrigo, Fereshteh Yousefirizi, Ziping Liu, Abhinav Jha, Robert Bergen, Jean-Francois Rajotte, Raymond Ng, Ingrid Bloise, Sara Harsini, Dan Kadrmas, Carlos Uribe, Arman Rahmim

Proceedings Volume 12467, 124670S (2023) https://doi.org/10.1117/12.2652661

KEYWORDS: Positron emission tomography, Education and training, Cancer, Image segmentation, Tumors, Video, Artificial intelligence, Nuclear medicine, Medical imaging

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Proceedings Article | 3 April 2023 Poster + Paper

A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset

Shadab Ahamed, Yixi Xu, Ingrid Bloise, Joo H. O., Carlos Uribe, Rahul Dodhia, Juan Lavista Ferres, Arman Rahmim

Proceedings Volume 12464, 124641Q (2023) https://doi.org/10.1117/12.2652947

KEYWORDS: Education and training, Data modeling, Positron emission tomography, Tumors, Performance modeling, Binary data, Computed tomography, Lymphoma, Image segmentation, Image classification, X-ray computed tomography

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Proceedings Article | 3 April 2023 Poster + Paper

Revisiting the supervision level in semi-supervised learning for automated tumor segmentation: application to lymphoma FDG PET imaging

Fereshteh Yousefirizi, Joo Hyun O, Ingrid Bloise, Amirhosein Toosi, Carlos Uribe, Arman Rahmim

Proceedings Volume 12464, 124643C (2023) https://doi.org/10.1117/12.2654492

KEYWORDS: Education and training, Image segmentation, Positron emission tomography, Lymphoma, Data modeling, Tumors, Machine learning, 3D modeling, Voxels, Performance modeling

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Proceedings Article | 3 April 2023 Poster + Paper

State-of-the-art object detection algorithms for small lesion detection in PSMA PET: use of rotational maximum intensity projection (MIP) images

Amirhosein Toosi, Sara Harsini, Shadab Ahamed, Fereshteh Yousefirizi, François Bénard, Carlos Uribe, Arman Rahmim

Proceedings Volume 12464, 124643E (2023) https://doi.org/10.1117/12.2654527

KEYWORDS: Object detection, Principal component analysis, Education and training, Positron emission tomography, Detection and tracking algorithms, Image segmentation, Cancer detection, Evolutionary algorithms, Computer aided detection, 3D projection

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Proceedings Article | 4 April 2022 Presentation + Paper

Advanced survival prediction in head and neck cancer using hybrid machine learning systems and radiomics features

Mohammadreza Salmanpour, Mahdi Hosseinzadeh, Ensiyeh Modiri, Azizeh Akbari, Ghasem Hajianfar, Dariush Askari, Mehdi Fatan, Mehdi Maghsudi, Hanieh Ghaffari, Seyed Masoud Rezaeijo, Mohammad M. Ghaemi, Arman Rahmim

Proceedings Volume 12036, 1203614 (2022) https://doi.org/10.1117/12.2612816

KEYWORDS: Cancer, Positron emission tomography, Feature extraction, Machine learning, Feature selection, Discrete wavelet transforms, Computed tomography

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Accurate prognostic stratification of Head-and-Neck-Squamous-Cell-Carcinoma (HNSCC) patients can be an important clinical reference when designing therapeutic strategies. We set to predict 4 outcomes: overall survival (OS), distant metastasis (DM), locoregional recurrence (LR), and progression-free survival (SP). We studied Hybrid Machine Learning Systems (HMLS), applied to datasets with radiomics features. In this multicenter study, 408 HNSCC patients were extracted from The Cancer Imaging Archive (TCIA) database. PET images were registered to CT, enhanced, and cropped. 215 radiomics features were extracted from each region of interest via our standardized SERA radiomics package. We employed multiple HMLSs: 12 feature extraction (FEA) or 9 feature selection algorithms (FSA) linked with 9 survival-prediction-algorithms (SPA) optimized by 5-fold cross-validation, applied to PET only, CT only and 4 PET-CT datasets generated by image-level fusion strategies. Datasets were normalized by z-score-technique, and cindices were reported to compare the models. For OS prediction, the highest c-index 0.73 ± 0.10 was obtained for HMLS with Ratio of low-pass pyramid (RP) fusion technique + gaussian process latent variable model (GPLVM) + causal structure learning-based feature modification method (CSFM). For DM prediction, we achieved 0.80±0.06 via Dual-tree complex wavelet transform (DTCWT) fusion + Laplacian Score (LAP) + Logistic regression hazards (LH). For LR prediction, we arrived at a c-index of 0.73 ± 0.13 using PET + Sammon Mapping Algorithm (SM)+ deep neural network to distribute first hitting times (DHS). For SP prediction, the performance of 0.68 ± 0.02 was obtained via PET + SM + Relative risk model-depend on time (CoxTime). When no dimensionality reduction (FEA/FSA) was employed, the above 4 performances decreased to 0.69 ± 0. 10, 0.74 ± 0.13, 0.66 ± 0.15, and 0.68 ± 0.04 for OS, DM, LR and SP prediction. We demonstrated that using fusion techniques followed by appropriate HMLSs, including FEAs/FSAs and SPAs, improved prediction performance.

Proceedings Article | 4 April 2022 Presentation + Paper

Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images

Fereshteh Yousefirizi, Natalia Dubljevic, Shadab Ahamed, Ingrid Bloise, Claire Gowdy, Joo Hyun O, Youssef Farag, Rodrigue de Schaetzen, Patrick Martineau, Don Wilson, Carlos Uribe, Arman Rahmim

Proceedings Volume 12032, 120320V (2022) https://doi.org/10.1117/12.2612675

KEYWORDS: Image segmentation, Lymphoma, 3D modeling, Positron emission tomography, Data modeling, Brain, Bladder, Cancer, Convolutional neural networks, Tumors

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Proceedings Article | 4 April 2022 Poster + Paper

A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma

Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise, Claire Gowdy, Patrick Martineau, Don Wilson, Carlos Uribe, Arman Rahmim, Fereshteh Yousefirizi

Proceedings Volume 12032, 120323M (2022) https://doi.org/10.1117/12.2612684

KEYWORDS: Machine learning, Convolutional neural networks, Digital image processing, Cancer, Image segmentation, Tumors, Positron emission tomography, 3D modeling, Sensors, Lymphoma, 3D image processing, Network architectures, Performance modeling, Image classification

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Proceedings Article | 4 April 2022 Presentation + Paper

3D PET image generation with tumour masks using TGAN

Robert Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi, Ivan Klyuzhin, Arman Rahmim, Raymond Ng

Proceedings Volume 12032, 120321P (2022) https://doi.org/10.1117/12.2611292

KEYWORDS: Image segmentation, Positron emission tomography, Data modeling, 3D image processing, Medical imaging, Video, Head

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Proceedings Article | 4 April 2022 Poster + Paper

Multi-modality fusion coupled with deep learning for improved outcome prediction in head and neck cancer

Arash Javanmardi, Mahdi Hosseinzadeh, Ghasem Hajianfar, Amir Hossein Nabizadeh, Seyed Masoud Rezaeijo, Arman Rahmim, Mohammadreza Salmanpour

Proceedings Volume 12032, 120322I (2022) https://doi.org/10.1117/12.2612833

KEYWORDS: Image fusion, Positron emission tomography, Computed tomography, Cancer, Discrete wavelet transforms, Tumors, Neck, Head, Magnetic resonance imaging, Machine learning

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Proceedings Article | 4 April 2022 Poster + Paper

Prediction of TNM stage in head and neck cancer using hybrid machine learning systems and radiomics features

Mohammad Salmanpour, Mahdi Hosseinzadeh, Azizeh Akbari, Kasra Borazjani, Kasra Mojallal, Dariush Askari, Ghasem Hajianfar, Seyed Masoud Rezaeijo, Mohammad M. Ghaemi, Amir Hossein Nabizadeh, Arman Rahmim

Proceedings Volume 12033, 120332H (2022) https://doi.org/10.1117/12.2612998

KEYWORDS: Cancer, Positron emission tomography, Feature selection, Computed tomography, Machine learning, Evolutionary algorithms, Tumors, Feature extraction

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The tumor, node, metastasis (TNM) staging system enables clinicians to describe the spread of head-and-necksquamous-cell-carcinoma (HNSCC) cancer in a specific manner to assist with the assessment of disease status, prognosis, and management. This study aims to predict TNM staging for HNSCC cancer via Hybrid Machine Learning Systems (HMLSs) and radiomics features. In our study, 408 patients were included from the Cancer Imaging Archive (TCIA) database, included in a multi-center setting. PET images were registered to CT, enhanced, and cropped. We created 9 sets including CT-only, PET-only, and 7 PET-CT fusion sets. Next, 215 radiomics features were extracted from HNSCC tumor segmented by the physician via our standardized SERA radiomics package. We employed multiple HMLSs, including 16 feature-extraction (FEA) + 9 feature selection algorithms (FSA) linked with 8 classifiers optimized by grid-search approach, with model training, fine-tuning, and selection (5-fold cross-validation; 319 patients), followed by external-testing of selected model (89 patients). Datasets were normalized by z-score-technique, with accuracy reported to compare models. We first applied datasets with all features to classifiers only; accuracy of 0.69 ± 0.06 was achieved via PET applied to Random Forest classifier (RFC); performance of external testing (~ 0.62) confirmed our finding. Subsequently, we employed FSAs/FEAs prior to the application of classifiers. We achieved accuracy of 0.70 ± 0.03 for Curvelet transform (fusion) + Correlation-based Feature Selection (FSA) + K-Nearest Neighbor (classifier), and 0.70 ± 0.05 for PET + LASSO (FSA) + RFC (classifier). Accuracy of external testing (0.65 & 0.64) also confirmed these findings. Other HMLSs, applied on some fused datasets, also resulted in close performances. We demonstrate that classifiers or HMLSs linked with PET only and PET-CT fusion techniques enabled relatively low improved accuracy in predicting TNM stage. Meanwhile, the combination of PET and RFC enabled good prediction of TNM in HNSCC.

Proceedings Article | 10 April 2019 Presentation + Paper

CNN and back-projection: limited angle ultrasound tomography for speed of sound estimation

Emran Mohammad Abu Anas, Alexis Cheng, Reza Seifabadi, Yixuan Wu, Fereshteh Aalamifar, Bradford Wood, Arman Rahmim, Emad Boctor

Proceedings Volume 10955, 109550M (2019) https://doi.org/10.1117/12.2513043

KEYWORDS: Linear filtering, Tomography, Tissues, Receivers, Ultrasound tomography, Nonlinear filtering, Transmitters, Neural networks, Wave propagation, Transducers

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Proceedings Article | 15 March 2019 Presentation + Paper

Correlation of ultrasound tomography to MRI and pathology for the detection of prostate cancer

Reza Seifabadi, Alexis Cheng, Bilal Malik, Shun Kishimoto, James Wiskin, Jeeva Munasinghe, Ayele Negussie, Ivane Bakhutashvili, Murali Krishna, Peter Choyke, Peter Pinto, Arman Rahmim, Emad Boctor, Maria Merino, Mark Lenox, Baris Turkbey, Bradford Wood

Proceedings Volume 10955, 109550C (2019) https://doi.org/10.1117/12.2512001

KEYWORDS: Prostate, Magnetic resonance imaging, Tissues, Pathology, Cancer, Prostate cancer, Ultrasound tomography, Tomography

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Proceedings Article | 15 March 2019 Paper

Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer

Alexis Cheng, Younsu Kim, Emran M. Anas, Arman Rahmim, Emad Boctor, Reza Seifabadi, Bradford Wood

Proceedings Volume 10955, 1095516 (2019) https://doi.org/10.1117/12.2512533

KEYWORDS: Image restoration, Prostate, Ultrasound tomography, Prostate cancer, Image sensors, Biopsy, Sensors, Acoustics, Transducers, Magnetic resonance imaging

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Proceedings Article | 22 August 2017 Paper

Toward high-speed transcranial photoacoustic imaging using compact near-infrared pulsed LED illumination system

Jeeun Kang, Haichong Zhang, Arman Rahmim, Dean Wong, Jin Kang, Emad Boctor

Proceedings Volume 10064, 100643B (2017) https://doi.org/10.1117/12.2253322

KEYWORDS: Imaging systems, Brain, LED lighting, Signal to noise ratio, Photoacoustic imaging, Photoacoustic spectroscopy, Functional imaging, Neuroimaging, Skull, Bone

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SPIE Journal Paper | 10 April 2017 Open Access

Listening to membrane potential: photoacoustic voltage-sensitive dye recording

Haichong Zhang, Ping Yan, Jeeun Kang, Diane Abou, Hanh N. Le, Abhinav Jha, Daniel L. Thorek, Jin Kang, Arman Rahmim, Dean Wong, Emad Boctor, Leslie Loew

JBO, Vol. 22, Issue 04, 045006, (April 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.4.045006

KEYWORDS: Luminescence, Absorbance, Photoacoustic spectroscopy, Voltage sensitive dyes, Brain, Ultrasonography, Neuroimaging, Quantum efficiency, Chromophores, Polarization

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Proceedings Article | 22 March 2017 Presentation + Paper

Recording membrane potential changes through photoacoustic voltage sensitive dye

Haichong Zhang, Jeeun Kang, Ping Yan, Diane Abou, Hanh N. Le, Daniel L. Thorek, Jin Kang, Albert Gjedde, Arman Rahmim, Dean Wong, Leslie Loew, Emad Boctor

Proceedings Volume 10064, 1006407 (2017) https://doi.org/10.1117/12.2252442

KEYWORDS: Photoacoustic spectroscopy, Voltage sensitive dyes, Photoacoustic imaging, Deep tissue imaging, Near infrared, Ultrasonography, Brain, Absorbance, Quantum efficiency, Neuroimaging, Chromophores

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Proceedings Article | 13 March 2017 Presentation + Paper

A radiative transfer equation-based image-reconstruction method incorporating boundary conditions for diffuse optical imaging

Abhinav Jha, Yansong Zhu, Dean Wong, Arman Rahmim

Proceedings Volume 10137, 1013705 (2017) https://doi.org/10.1117/12.2255802

KEYWORDS: Brain, Refractive index, Neuroimaging, Reconstruction algorithms, Diffuse optical imaging, 3D modeling, Scattering, Optical imaging, Tissue optics, Laser scattering, Sensors, Absorption, Reflection

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SPIE Journal Paper | 3 March 2017

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

Abhinav Jha, Esther Mena, Brian Caffo, Saeed Ashrafinia, Arman Rahmim, Eric Frey, Rathan Subramaniam

JMI, Vol. 4, Issue 01, 011011, (March 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.1.011011

KEYWORDS: Video, Video compression, Echocardiography, Motion measurement, Video processing, Image segmentation, Image quality, Diagnostics, Image processing, Motion estimation

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Proceedings Article | 17 February 2017 Paper

A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing

Yansong Zhu, Abhinav Jha, Jakob Dreyer, Hanh N. Le, Jin Kang, Per Roland, Dean Wong, Arman Rahmim

Proceedings Volume 10059, 1005911 (2017) https://doi.org/10.1117/12.2252664

KEYWORDS: Tomography, Reconstruction algorithms, Fluorescence tomography, Expectation maximization algorithms, Compressed sensing, Brain, Scattering, In vivo imaging, Data acquisition, Sensors

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Proceedings Article | 8 February 2017 Presentation + Paper

Through-skull vasculature assessment using fluorescence brain imaging on murine models at around 800 nm

Hanh N. Le, Yung-Tian Gau, Arman Rahmim, Dean Wong, Dwight Bergles, Jin Kang

Proceedings Volume 10051, 100510M (2017) https://doi.org/10.1117/12.2250240

KEYWORDS: Luminescence, Brain imaging, Spatial resolution, Scattering, Signal to noise ratio, Tissues, Skull, Imaging systems, CMOS sensors, Bone, Brain, Cameras, Scattering media

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Proceedings Article | 1 April 2016 Paper

Image reconstruction for robot assisted ultrasound tomography

Fereshteh Aalamifar, Haichong Zhang, Arman Rahmim, Emad Boctor

Proceedings Volume 9790, 979017 (2016) https://doi.org/10.1117/12.2216518

KEYWORDS: Tomography, Ultrasonography, Ultrasound tomography, Robotics, Image restoration, Breast imaging, Cancer, Kidney, Liver, Receivers, Transmitters, Water, Transducers, Data acquisition

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Proceedings Article | 19 March 2014 Paper

Towards continualized task-based resolution modeling in PET imaging

Saeed Ashrafinia, Nicolas Karakatsanis, Hassan Mohy-ud-Din, Arman Rahmim

Proceedings Volume 9033, 903327 (2014) https://doi.org/10.1117/12.2043973

KEYWORDS: Point spread functions, Signal to noise ratio, Positron emission tomography, Image resolution, Tumors, Signal detection, Image filtering, Lung, Tumor growth modeling, Interference (communication)

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Proceedings Article | 19 March 2014 Paper

Whole-body PET parametric imaging employing direct 4D nested reconstruction and a generalized non-linear Patlak model

Nicolas Karakatsanis, Arman Rahmim

Proceedings Volume 9033, 90330Y (2014) https://doi.org/10.1117/12.2043918

KEYWORDS: Reconstruction algorithms, Positron emission tomography, Data modeling, Signal to noise ratio, Tumors, Image quality standards, Image enhancement, Blood, 3D modeling, Algorithm development

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Proceedings Article | 13 March 2014 Paper

Parametric myocardial perfusion PET imaging using physiological clustering

Hassan Mohy-ud-Din, Nikolaos Karakatsanis, Martin Lodge, Jing Tang, Arman Rahmim

Proceedings Volume 9038, 90380P (2014) https://doi.org/10.1117/12.2043947

KEYWORDS: Positron emission tomography, Blood, Tissues, Arteries, Signal to noise ratio, Physiology, Image processing, Spatial resolution, Image resolution, Reconstruction algorithms

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Showing 5 of 25 publications

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